Demo Data from EDBT 2026 / reviewers in the wild / expert
Mingqian Zhang
dblp:287/5212
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › computational microbiology › microbiome analysis
beta-diversity analysis |
0.7 | 1 | 2023 | Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes · Bioinform. 2023 |
Bioinformatics and computational biology › computational microbiology
microbiome analysis |
0.7 | 1 | 2023 | Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypes · Bioinform. 2023 |
Methods — techniques the papers use, named apart from their topics
phylogenetic distance · 0.7local alignment · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | BMFNet: Bifurcated multi-modal fusion network for RGB-D salient object detection
Chenwang Sun, Qing Zhang 0004, Chenyu Zhuang, Mingqian Zhang |
Image Vis. Comput. | 4 |
| 2023 | Flex Meta-Storms elucidates the microbiome local beta-diversity under specific phenotypesabstractMOTIVATION: Beta-diversity quantitatively measures the difference among microbial communities thus enlightening the association between microbiome composition and environment properties or host phenotypes. The beta-diversity analysis mainly relies on distances among microbiomes that are calculated by all microbial features. However, in some cases, only a small fraction of members in a community plays crucial roles. Such a tiny proportion is insufficient to alter the overall distance, which is always missed by end-to-end comparison. On the other hand, beta-diversity pattern can also be interfered due to the data sparsity when only focusing on nonabundant microbes. RESULTS: Here, we develop Flex Meta-Storms (FMS) distance algorithm that implements the "local alignment" of microbiomes for the first time. Using a flexible extraction that considers the weighted phylogenetic and functional relations of microbes, FMS produces a normalized phylogenetic distance among members of interest for microbiome pairs. We demonstrated the advantage of FMS in detecting the subtle variations of microbiomes among different states using artificial and real datasets, which were neglected by regular distance metrics. Therefore, FMS effectively discriminates microbiomes with higher sensitivity and flexibility, thus contributing to in-depth comprehension of microbe-host interactions, as well as promoting the utilization of microbiome data such as disease screening and prediction. AVAILABILITY AND IMPLEMENTATION: FMS is implemented in C++, and the source code is released at https://github.com/qdu-bioinfo/flex-meta-storms. Mingqian Zhang, Wenke Zhang, Shunyao Wu, Xiaoquan Su |
Bioinform. | 1 |